Model comparison
Qwen3 14B vs Qwen3.5-9B
Qwen3 14B is the stronger model overall, scoring 35.5 to 33.8 on the Noometry Index. Qwen3.5-9B costs 5.4× less per token, which makes it the better buy when Qwen3 14B's lead doesn't matter for your workload.
Last verified . 8 shared benchmarks.
Alibaba (Qwen)Summary
- They share 8 benchmarks with published results for both. Qwen3 14B scores higher in 3 categories and Qwen3.5-9B in 2 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where Qwen3 14B leads 29.6 to 14.5.
- The biggest single-benchmark swing is GPQA Diamond: 63.8% for Qwen3 14B and 79% for Qwen3.5-9B.
- Qwen3.5-9B is cheaper at $0.10 / $0.15 per million input/output tokens, against $0.35 / $1.40 for Qwen3 14B.
- Qwen3.5-9B accepts more context: 262K tokens versus 131K.
Side by side
| Qwen3 14B | Qwen3.5-9B | |
|---|---|---|
| Provider | Alibaba (Qwen) | Alibaba (Qwen) |
| Noometry Index | 35.5 | 33.8 |
| Released | 2025-04 | 2026-02-23 |
| Weights | Open | Open |
| Context window | 131K | 262K |
| Max output | 8K | 66K |
| Input $ / M tokens | $0.35 | $0.10 |
| Output $ / M tokens | $1.40 | $0.15 |
| Results tracked | 12 | 10 |
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Category by category
Coding Qwen3 14B leads
Qwen3 14B: 37.3 (#195), Qwen3.5-9B: 35.9 (#217)
| Benchmark | Qwen3 14B | Qwen3.5-9B |
|---|---|---|
| SciCode | 31.6% | 27.5% |
Agentic & Tool Use Qwen3 14B leads
Qwen3 14B: 29.6 (#83), Qwen3.5-9B: 14.5 (#151)
| Benchmark | Qwen3 14B | Qwen3.5-9B |
|---|---|---|
| Terminal-Bench | — | 9.2% |
| Berkeley Function Calling Leaderboard | 41% | — |
Reasoning Qwen3.5-9B leads
Qwen3 14B: 18.5 (#280), Qwen3.5-9B: 23.1 (#182)
| Benchmark | Qwen3 14B | Qwen3.5-9B |
|---|---|---|
| CritPt | 0% | 0.3% |
| Chess Puzzles | 4% | 12% |
| DTBench | 64% | 71.2% |
| LMCA | 18.2% | 24.5% |
| Epoch Capabilities Index | 138.23 | 139.46 |
| Kagi LLM Benchmark | 49.1% | — |
Math Qwen3 14B leads
Qwen3 14B: 38.6 (#133), Qwen3.5-9B: 34.8 (#192)
| Benchmark | Qwen3 14B | Qwen3.5-9B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | 61.7% |
| MathArena Final-Answer Competitions | — | 48.5% |
Knowledge Qwen3.5-9B leads
Qwen3 14B: 39.3 (#134), Qwen3.5-9B: 46.0 (#84)
| Benchmark | Qwen3 14B | Qwen3.5-9B |
|---|---|---|
| GPQA Diamond | 63.8% | 79% |
| Vectara Hallucination Rate | 5.4% | — |
Long Context Not comparable
Qwen3 14B: 38.1 (#204), Qwen3.5-9B: —
| Benchmark | Qwen3 14B | Qwen3.5-9B |
|---|---|---|
| Fiction.LiveBench | 62.5% | — |
Frequently asked questions
Is Qwen3 14B better than Qwen3.5-9B?
Qwen3 14B is the stronger model overall, scoring 35.5 to 33.8 on the Noometry Index. Qwen3.5-9B costs 5.4× less per token, which makes it the better buy when Qwen3 14B's lead doesn't matter for your workload.
Which is cheaper, Qwen3 14B or Qwen3.5-9B?
Qwen3.5-9B is cheaper. It lists at $0.10 per million input tokens and $0.15 per million output tokens; Qwen3 14B lists at $0.35 and $1.40.
Is Qwen3 14B or Qwen3.5-9B better for coding?
Qwen3 14B scores higher on coding benchmarks: 37.3 versus 35.9 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5-9B does, with 262K tokens against 131K.
How many benchmarks do Qwen3 14B and Qwen3.5-9B share?
8 benchmarks have published results for both models. Qwen3 14B has 12 scored results on Noometry and Qwen3.5-9B has 10.